(1) Field of Invention
The present invention relates to a knowledge-enhanced compressive imaging system and, more particularly, to a knowledge-enhanced compressive imaging system which uses a priori knowledge and compressive measurement (CM) techniques to reduce the number of data samples required.
(2) Description of Related Art
Data compression allows for compact storage and rapid transmission of large amounts of data. Compressed sensing is a technique for finding sparse solutions to underdetermined linear systems. In signal processing, compressed sensing is the process of acquiring and reconstructing a signal that is supposed to be sparse or compressible. In the field of image compression, current imaging systems perform direct Nyquist sampling of optical images using sensor arrays in which each detector element records a single pixel in the image. Each pixel is then digitized and stored in an array of pixel values, which can then be compressed. The Nyquist sampling theorem shows that a bandlimited analog signal that has been sampled can be perfectly reconstructed from a sequence of samples if the sampling rate exceeds 2B samples per unit distance, where B is the highest spatial frequency in the original signal. Conventional sampling is not adaptive to the task to be performed and is inefficient in hardware and computational resource utilization, because it stores information that is not necessarily needed for a particular task.
In addition, image resolution is limited by the physical number of detector elements, resulting in ever larger sensor arrays and onboard size, weight and power (SWAP)/bandwidth requirements as mission requirements increase. Compressive measurement (CM) has been a demonstrated viable alternative to Nyquist sampling by taking advantage of the fact that images have sparse representations for certain sets of basis functions. CM has been used to reconstruct images using far fewer measurements than predicted by the Nyquist sampling criterion by pre-processing with random measurement matrices and using sparity-enforcing optimization methods to reconstruct the image. Existing CM imaging systems, however, do not take advantage of prior knowledge about the data or the task to be performed with the imagery or real-time adaptation to the data, which limits the amount of SWAP and bandwidth reduction that can be achieved.
Thus, a continuing need exists for a knowledge-enhanced compressive imaging system that reduces the number of physical measurements needed to achieve a given level of task performance, thereby greatly increasing the utility of the sensing system while reducing SWAP and communication bandwidth requirements.